Optimizing CDN Infrastructure for Live Streaming with Constrained Server Chaining

Zhuang, Zhenyun; Guo, Chun

doi:10.1109/ispa.2011.44

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…None of existing algorithms can simultaneously satisfy these design requirements of the resource allocation in the global scale. For example, [8], [12], [18] considered the viewer dynamic in the live streaming systems, without analysis of the demand distribution in the different areas; though [11], [15], [9] deployed the distributed cloud servers or CDNs to handle the dynamic demand in the global scale, crowdsourced live streaming further elevate the challenges with highly dynamic and geo-distributed sourcers; [10], [14] explored the cloud service migration for video-on-demand, while live streaming raises more stringent delay constraint during the server resource allocation; [13] investigated the joint online transcoding and geo-distributed delivery for dynamic adaptive streaming neglecting the price policies of different datacenters. Generally, the resource allocation in crowdsourced live streaming should not only consider both the dynamic viewers and sourcers, but also balance the trade-off between the streaming performance and the system cost.…”

Section: Background and Related Workmentioning

confidence: 99%

Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content

Chen

Zhang

Wang

et al. 2015

IEEE Trans. Multimedia

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Empowered by today's rich tools for media generation and distribution, and the convenient Internet access, streaming crowdsourced multimedia content (crowdsourced streaming, in brief) generalizes the single-source streaming paradigm by including massive contributors for a video/data channel. It calls a joint optimization along the path from crowdsourcers, through streaming servers, to the end-users to minimize the overall latency. The dynamics of the video sources, together with the globalized request demands and the high computation demand from each sourcer, make crowdsourced live streaming challenging even with powerful support from modern cloud computing. In this paper, we present a generic framework that facilitates a cost-effective cloud service for crowdsourced live streaming. Through adaptively leasing, the cloud servers can be provisioned in a fine granularity to accommodate geo-distributed video crowdsourcers. We present an optimal solution to deal with service migration among cloud instances of diverse lease prices. It also addresses the location impact to the streaming quality. To understand the performance of the proposed strategies in the real world, we have built a prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our extensive experiments demonstrate that the effectiveness of our solution in terms of deployment cost and streaming quality.

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Section: Background and Related Workmentioning

confidence: 99%

Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content

Chen

Zhang

Wang

et al. 2015

IEEE Trans. Multimedia

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“…• Constrained Server Chaining (CSC) [7], whose objective function is to minimize the source-to-end delay. This is a pull-based algorithm.…”

Section: A Simulation Setup and Performance Metricsmentioning

confidence: 99%

“…We briefly review related work here. While much work has been done on peer-to-peer (P2P) live streaming, most of them [2]- [7] focus on single-source single-channel overlay streaming. These works cannot be extended to multi-source multi-channel live streaming due to the shared resources such as link capacity and server upload bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Delay optimization for Multi-source Multi-channel Overlay live Streaming

Dai

Chang

Chan

2015

2015 IEEE International Conference on Communications (ICC)

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Abstract-In order to provide scalable live streaming service, a content provider often deploys an overlay cloud consisting of distributed servers which collaboratively exchange streams with each other. We consider an overlay consisting of multiple live channels originating from multiple sources. Server bandwidth and end-to-end network bandwidth are shared among these channels. The stream of each channel is divided into multiple substreams of a certain bitrate, each of which is pushed via a tree to the servers that subscribe to the channel. The critical and challenging issue is then how to optimize the topology of the substream trees so as to minimize the maximum channel delay (defined as the delay from the source to the subscribing servers).There has been little work on the optimization of such multisource multi-channel live streaming network. We first formulate the problem which comprehensively and realistically captures various delay and bandwidth components, and show that it is NP-hard. We then propose an efficient algorithm called COMMOS (Collaborative Multi-source Multi-channel Streaming Overlay) which achieves low channel delay. Extensive simulation results based on real Internet topologies show that COMMOS outperforms other state-of-the-art schemes by a wide margin (often by more than 40%), due to its better utilization of network resources.

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“…This objective is outdated since CDNs and ISPs develop peering agreements, which reduce the importance of the bandwidth cost. In [37], the authors proposed a flat-layered CDN infrastructure for live streaming. Whereas we still consider the commercial widely used layered infrastructure, so that our approaches have the potential benefits of being implemented in real CDNs.…”

Section: Related Workmentioning

confidence: 99%